In an experiment to determine the factors affecting tensile strength in steel plates, the tensilestrength (in kg/mm?), the manganese content (in parts per thousand), and the thickness (inmm) were measured for a sample of 20 plates. The following MINITAB output presents theresults of fitting the model Tensile strength = Bo + B1 Manganese + B2 Thickness.The regression equation isStrength = 26.641 + 3.3201 Manganese – 0.4249 ThicknessPredictorCoef StDevт РConstant26.6412.723409.78 0.000Manganese 3.32010.3319810.00 0.000Thickness -0.42490.12606 -3.37 0.004S = 0.8228 R-Sq = 86.2% R-Sq(adj) =84.6%Analysis of VarianceSS MS2 72.0136.005 53.190.000SourceDFRegressionResidual Error 1711.508 0.6769Total1983.517Predict the strength for a specimen that is 10 mm thick and contains 8.2 ppta.manganese.b. If two specimens have the same thickness, and one contains 10 ppt more manganese,by how much would you predict their strengths to differ?If two specimens have the same proportion of manganese, and one is 5 mm thickerthan the other, by how much would you predict their strengths to differ?C.

Answered: In an experiment to determine the…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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A manager at a local bank analyzed the relationship between monthly salary and three independent variables: Length of service (measured in months), Gender (0 = female, 1= male), and Job type (0 = clerical, 1 = technical). The following ANOVA summarizes the %3D regression results. ANOVA Source of Variation df Sum of Squares Mean Square F Regression 3 1,004,346.771 334,782.257 5.96 Residual 26 1,461,134.596 56,197.48445 Total 29 2,465,481.367 Coefficients Standard Error t-Stat p-value Intercept 784.92 322.25 2.44 0.02 Service 9.19 3.20 2.87 0.01 Gender 222.78 89.00 2.50 0.02 Job -28.21 89.61 -0.31 0.76 The level of significance is 0.05. In the regression model, which of the following are dummy variables?
Consider a regression that uses X = # of 60 pound bench press repetitions to predict Y = maximum bench press amount in pounds…..for the ith student among a sample of high school athletes. The estimated predicted sample regression is as follows Maxi= 68.91 + .6885Repsi *the ei is left off for simplicity The table below lists the actual data from which the regression above was estimated. For example, a student in the study who trained by doing 12 “60 pound reps” accomplished a maximum bench press amount of 76 pounds. Xi Yi 12 76 15 82 17 79 3 71 Predict the average maximum bench press for a student that does 17 “60 pound reps” a) a. around 68.85 pounds b) b. around 85.00 pounds c) c. around 80.61 pounds d) d. around 68.91 pounds
A ski resort asked a random sample of guests to rate their satisfaction on various attributes of their visit on a scale of 1–5 with 1 = very unsatisfied and 5 = very satisfied. The estimated regression model was Y = overall satisfaction score, X1 = lift line wait, X2 = amount of ski trail grooming, X3 = safety patrol visibility, and X4 = friendliness of guest services. Predictor Coefficient Intercept 2.9018 LiftWait 0.1642 AmountGroomed 0.2343 SkiPatrolVisibility 0.0602 FriendlinessHosts −0.1193 (a) Write the fitted regression equation. (Round your answers to 4 decimal places. Negative values should be indicated by a minus sign.) yˆy^ = + * LiftWait + * AmountGroomed + * SkiPatrolVisibility + * FriendlinessHosts (b) Interpret each coefficient. Overall satisfaction (Click to select) increases decreases remains same with an increase in satisfaction for each individual predictor except for friendliness of hosts.(c) Would the intercept seem to…
When the heights (in inches) and shoe lengths (also in inches) were measured for a large random sample of individuals, it was found that r = 0.89, and a regression equation was constructed in order to further explore the relationship between shoe length and height, with height being the response variable. From this information, what can we conclude? The correlation coefficient should have no units. The regression equation relating shoe length to height must have a slope equal to 0.89. The regression equation relating shoe length to height must have a positive intercept. O Approximately 89% of the variability in height can be explained by the regression equation. Because the value of r is less than 1, we should characterize this relationship as being weak.
From a regression equation r2= 0.39 and the slope = -2.8 What is the linear correlation coefficient r?
A regression model to predict Y, the state burglary rate per 100,000 people, used the following four state predictors: X1 = median age, X2 = number of bankruptcies per 1,000 population, X3 = federal expenditures per capita (a leading predictor), and X4 = high school graduation percentage. Click here for the Excel Data File (a) Using the sample size of 45 people, calculate the tcalc and p-value in the table given below. (Negative values should be indicated by a minus sign. Leave no cells blank - be certain to enter "0" wherever required. Round your t-values to 3 decimal places and p- values to 4 decimal places.) Predictor Intercept AgeMed Coefficient SE tcalc p-value 4,641.0430 798.0634 -28.8630 12.4684 Bankrupt 20.1604 12.1079 FedSpend HSGrad% -0.0181 0.0181 -30.3196 7.1136 (b-1) What is the critical value of Student's tin Appendix D for a two-tailed test at a = .01? (Round your answer to 3 decimal places.) -value =
a) what is the regression equation? b) what is the predicted weight of the bear? c) is the result close to the actual weight of 528 pounds?
Use the given data to find the best predicted value of the response variable. Use a significance level of 0.05The regression equation relating attitude rating (x) and job performance rating (y) for the employees of a company is y=11.3+1.24x. Ten pairs of data were used to obtain the equation. The same data yield r=0.852 and y¯=80.14. What is the best predicted job performance rating for a person whose attitude rating is 72?
A real estate builder wishes to determine how house size (House) is influenced by family income (Income) and family size (Size). House size is measured in meter square and income is measured in IDR millions. The builder randomly selected 50 families and ran the multiple regression. Partial Microsoft Excel output is provided below: Which of the independent variables in the model are significant at the 5% level? Formulate the hypothesis and explain the answer. How far can you rely upon this model? Or, what is the percentage variation in House explained by the model? What is the predicted house size (in hundreds of square feet) for an individual earning an annual income of IDR 400 million and having a family size of 4?
Listed below are altitudes (thousands of feet) and outside air temperatures (°F) recorded during a flight. Find the (a) explained variation, (b) unexplained variation, and (c) indicated prediction interval. There is sufficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. For the prediction interval, use a 95% confidence level with the altitude of 6327 ft (or 6.327 thousand feet). Altitude 8 15 22 28 31 33 Temperature 56 39 24 - 28 - 41 - 60 a. Find the explained variation. (Round to two decimal places as needed.)
For 40 eruptions of the Old Faithful geyser in Yellowstone National Park, duration times (sec) were recorded along with the heights (ft) of the eruptions. The linear correlation coefficient is r = 0.0915 and the regression equation is = 119 + 0.0331x, where x represents duration time. The mean of the 40 duration times is 245.0 sec and the mean of the 40 heights is 127.2 ft. What is the best predicted height of an eruption with a duration time of 120 min?
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